Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation.

The proposed approach is crucial because it addresses the identified research gap in HRES's intelligent power management control.It offers a dynamic and adaptive solution, optimizing energy distribution and balancing multiple sources effectively.The emphasis on reducing reliance on non-renewable backup sources aligns with sustainability goals, making the proposed IPMC a valuable contribution to the field.The comprehensive evaluation methodology ensures the practical applicability and reliability of the proposed solution in real-world scenarios.Overall, the need for the proposed approach stems from its potential to enhance the resilience, stability, and efficiency of hybrid renewable energy systems.
The hybrid power system discussed in this work comprises PV panels, a wind turbine, with a diesel generator and battery storage.This mix of energy sources allows for a more robust and versatile power generation system.The employment of a power flow or supervisory approach facilitates the management of the various power sources.This technique has been mentioned in past investigations 1,8,9,43,47 .The method is described as simple, quick, easy to implement, and does not involve heavy computations.The principal purpose of the proposed IPMC is to meet the load power needs.A secondary purpose is to keep the battery charged at a level that prevents blackouts and extends the overall lifespan of the battery.This dual-goal approach emphasizes the importance of both supplying immediate power needs and ensuring long-term stability and reliability.
Challenges arise from the variability of solar irradiation and wind availability, impacting the reliable and consistent delivery of energy.Current solutions involve backup mechanisms and energy storage, often relying on conventional fossil fuels.Efforts to minimize this dependence and enhance the reliability of HRES systems are ongoing, emphasizing the need for advanced control strategies.In this context, the proposed IPMC using FLC aims to optimize the performance of energy sources, extend their lifespan, and ensure continuous power supply.Unlike existing strategies, the IPMC considers variations in climatic conditions and efficiently balances multiple power sources.The contribution of this study lies in the development of a dynamic and adaptive IPMC solution tailored to the challenges specific to HRES.The study employs MATLAB/Simulink simulations and real-time findings from the RT-LAB simulator, providing a comprehensive evaluation.The proposed IPMC not only addresses current challenges in HRES but also contributes to the advancement of intelligent power management strategies for sustainable and efficient energy systems.

Studied hybrid system
The hybrid system integrates solar and wind sources, a diesel generator and batteries for storage (Fig. 1).Hybridization of wind and solar energy aims to leverage the complementary nature of these sources, considering their intermittent nature.A diesel backup generator is included in the system to provide additional power during low energy production or high demand, ensuring continuous power availability.Also, batteries play a crucial role in storing excess energy during times of high renewable energy production and releasing it when energy demand exceeds the current production.Diesel backup generators and batteries help to ensure a steady and reliable power supply, especially during times when renewable energy is scarce.The combination of wind and solar energy sources, coupled with backup capabilities from the diesel generator and energy storage, provides a more robust and resilient power generation system.

Photovoltaic model
Mathematical models are quite important in understanding and predicting the behavior of photovoltaic (PV) generators.The model to be used is determined by the amount of precision required, the complexity of the simulation, and the data available for parameterization.Each model has its strengths and limitations 68,69 .Table 2 gives the advantages and the drawbacks of the different types of mathematical models commonly used in PV generator modeling, each with its specific focus and application.
The one-diode model is commonly used in PV system modeling for several practical reasons as simplicity and accuracy, so it is used in our work.In this case, the electrical current is (Fig. 2) 1,8 : with: I ph the photo-current, I d the diode-current and I Rsh the shunt resistance R sh .
(1)    Based on experimental tests (Fig. 3), the parameters of a PV panel have been determined (Figs. 4 and 5) utilizing the electrical properties of PV (80Wp) 8 .Measurement sensors was used to measure the sun radiation, and temperature, to transfer the different signals to a data processing interface and then to a PC where they will be displayed using ACQUIsol software in real-time.
The established experimental bench is composed by an 80Wp panel (Table 3.), a voltmeter and an amperemeter with a variable load.The ambient temperature and solar irradiance are measured by using measurements devices.Extensive numerical simulations were carried out under MATLAB/Simulink environment.Runge Kutta of 4th order is used as a solver with a step of 1e−5.www.nature.com/scientificreports/

Wind turbine modeling
The system shown in Fig. 6. includes a wind turbine, suggesting the use of wind energy to drive a permanent magnet synchronous generator (PMSG).The different equations are 10,27,28 : where C p is the power coefficient, V wind the wind speed, λ the tip speed ratio, R the radius of the rotor radius, and ρ the density of the air.
The voltage equations are given as 37,38 : where: V ds and V qs are the stator voltages with the direct and quadrate axis, R s the stator winding resistance, I ds and I qs the stator currents with the direct and quadrate axis, L ds and L qs are the inductances with the direct and quadrate axis, P the number of pole pairs, ω the angular velocity, and Φ f the magnetic flux produced by the permanent magnet 70,71 .
The electromagnetic torque is written as 37,38 :

Battery storage modeling
The models can be used to simulate different scenarios and determine the most efficient and cost-effective ways to use the battery storage in conjunction with the other power sources 72,73 .Figure 7 depicts the model utilized in this investigation 1,8,9 : (2) An identification of the battery used of 12 V-100 Ah was carried out in the laboratory (Fig. 8).The battery is considered as an impedance Z batt with a resistance R batt and a reactance X batt .
The acquired results are R batt = 0.756 Ω and X batt = 0.072 Ω.

Diesel generator (DG) modeling
The complete diesel generator dynamic model involves modeling both the diesel engine with its speed control loop and the synchronous generator with its voltage control system (Fig. 9).The rotational speed error is the input of the speed controller, and the actuator control signal is its output.The droop δ d and the integrator factor K I are the parameters of the speed controller 74,75 .The goal of the integrator is to eliminate the static speed error.A first-order model with the gain K a and a time constant τ 2 is used to approximate the operational dynamics of the actuator.The fuel temperature affects this time factor.Although K a and τ 2 are both variable, their variation is negligible for short time periods.The equation of the synchronous motor mechanical is: With J the motor inertia, m the rotationnal speed, T d the diesel mechanical torque and T em the electromagnetic torque.( 7)

Sizing of the studied system
To obtain the appropriate size of each power source, such as the photovoltaic panels and wind turbine, the energy generation during each month of PV and wind generator and the load demand are calculated 8 .The PV and wind turbine generator areas are calculated from the ratios of the monthly energies: Then: with: The monthly energies produced are: The PV and wind generators areas will be finally: where k perc and (1 − k perc ) are respectively the fraction of the PV source and the fraction of the wind source 76 .
Finally, the calculated average load is determined by: The different findings are given in Tables 4 and 5.It can be concluded that only (10 panels and 01 wind turbine) configuration can be considered.The serial PV calculation is: www.nature.com/scientificreports/And PV maximum voltage will be: Thus the number of strings is: With E worst the worst solar energy irradiation at the studied site (3.5 kWh/m 2 day) and K loss represent the different losses.
Finally, the outcome is 5 strings.The battery capacity is 77,78 : With d aut the days of autonomy (days),E load,m the consumed monthly load (kWh/day), N m is equal to 31 days,U batt the voltage battery (V), PDP the depth of discharge and η batt the efficiency of the battery 79,80 .www.nature.com/scientificreports/ The number of batteries can be calculated as: With C batt-u the chosen battery capacity.
In our study, we have chosen a DG that delivers a constant voltage of 220 V, a current of 10 A and a power of 2 kVA.Table 6 summarizes all of the quantities that will be used.

Proposed intelligent power management control
The management method for autonomous hybrid systems is designed to fulfill load demand and control the power flow while offering the efficient operation of all energy sources.The IPMC approach prioritizes the use of photovoltaic and wind powers to meet the load requirement and relies on the use of long-term storage to supply the load.This helps reduce the start/stop cycles of a diesel generator which can indeed lead to lower fuel consumption and improve the energy balance of the system.By operating the generator for larger periods of time at a steady state, the energy losses that occur during the start-up and the shutdown can be minimized.Additionally, the load profile of the generator can be optimized to match the electricity demand, which can further improve fuel efficiency and reduce wear and tear on the generator.This is an important part of useful energy management because it can help to reduce running costs while also lowering the environmental impact of the system.The management approach is based on a cycle in which the diesel generator is turned off until the level of charge in the battery storage reaches a minimum, then the latter is restarted and continues running until the level of charge in the battery storage reaches a maximum, and the cycle is repeated.The equation of power balance is: The use of fuzzy logic improves overall system performance and efficiency through effective coordination and management of energy distribution.It can make cost-effective decisions on power source usage, optimize battery operation, and provide a stable and reliable power supply by coordinating the power sources, the diesel generator and the battery.The primary operation of the FLC is to create three control signals from three inputs (Fig. 10).The Mamdani methodology was used to build the fuzzy inference in this work 8 .
The inputs of the fuzzy regulator are listed in Table 7.
As illustrated in Table 8, it generates eight unique modes.Tables 9 and 10 indicate the relationship between each regulator input and the linguistic variables representing the fuzzy sets.

Simulation study
The controls used are designed to ensure that the voltages of PV panels and wind turbines are equal to the DC bus voltage.This helps to stabilize the system and extract the greatest amount of power, regardless of solar irradiance and wind speed variations.The control algorithms work to coordinate the power exchange between the various sources to ensure a stable and reliable power supply (Fig. 11).
Solar irradiation, ambient temperature, and wind speeds were measured using measurement acquisition equipment in the lab (Fig. 12).We have incorporated the recorded data from sun irradiation (Fig. 13), ambient temperature (Fig. 14), and wind speed (Fig. 15) in MATLAB/Simulink.
The OPAL RT LAB simulator is used for the studied system in real-time (Fig. 16).Simulations are run in Matlab/Simulink and then in real-time.
The load power is represented as follows (Fig. 17). Figure 18 presents the simulated voltage profile of the battery.The battery's voltage varies in accordance with the power absorbed/injected into the DC bus.
It is noticed in Fig. 19, battery SOC is well controlled and is maintained between 56.74 and 86.18%.The batteries SOCs are kept within bounds, regardless of the variations in PV, wind and load power profiles.
The different control signals generated by the IPMC with FLC are given in the Fig. 20.Figures 21 and 22 depict respectively the PV and wind powers during the twelve profiles.The PV power varies from 110.7 to 607.80 W while the wind power varies from 4.066 to 970.90 W.
Batteries and DG powers are represented in the same curve (Fig. 23) to show that the DG only starts when the batteries are discharged, i.e. when the battery power is zero.
This scenario depicts a system of energy sources that relies on wind, solar, batteries, and a backup generator to provide dependable power.The system is meticulously designed to minimize generator utilization, instead     relying on renewable sources, wind and solar, when available, and reserving the generator primarily for battery charging when required.This strategic approach serves to optimize energy consumption, reduce fuel consumption, and extend battery life.The power waveforms of the various sources are depicted in Fig. 24.Based on these findings, the proposed IPMC fulfills the load power need regardless of weather conditions.Figure 25 displays the total power consumed each day by all power sources for twelve different profiles.The PV power changes with solar irradiation profile.
To better depict discharges in relation to PV, wind and load changes, battery powers are presented in negative.It should be noted that the negative sign of the batteries' powers indicates that they are supplying power, while the positive sign indicates that they are been charged.At start of operations, the batteries are not fully charged, and though wind energy production is substantial, it falls short of meeting the load requirements, prompting the DG to activate and provide power (Profile 1t o 3).Notably, over the course of six consecutive profiles (Profiles 4 to 9), solar irradiance remains consistently at an average of approximately 500 W/m 2 .During this phase, batteries recharge during daylight hours and provide compensation when solar irradiance levels decrease.In Profile 10, the batteries become depleted, necessitating the DG to take over load supply, as wind power is no longer a significant contributor.In the final phase, during profiles 11 to 12, increased wind speeds and average solar irradiance levels facilitate battery charging and compensation using both photovoltaic and wind power sources and DG to supply the load.It is clear that the DG was only used during the battery charging phase, with the twin goal of protecting the batteries and extending their operational life.It may be inferred that the load power was satisfactory over the different twelve average profiles throughout a year, owing to accurate sizing and, in part, to the proposed IPMC.It is clear that the power discharge represents just a modest quantity (negative regions are highlighted in red).Notably, the simulation results closely match those of the real-time simulation.
The reference load power and the sum of power developed by all the power sources are respectively shown in Fig. 26.
The zoom of this last-mentioned figure for four distinct days is shown in Fig. 27.
In some cases, the computed power exceeds the power generated by the load.This surplus power is depicted in Fig. 27.Notably, even with adequate system sizing and the utilization of a Power Management Controller (PMC), a slight power surplus can be observed, during days of intense wind speeds and solar irradiance.These visual illustrations serve to demonstrate the effectiveness of the proposed control and energy management methodology in terms of state of charge, current profiles, operational modes, power generation and consumption, as well as alignment with load requirements.These graphical representations offer valuable insights into the system's performance under various conditions, confirming the viability and practicality of the research approach.

Conclusion
The study presents a promising approach to managing an autonomous hybrid energy system with a fuzzy logic controller.The novelty of the proposed IPMC lies in its dynamic and adaptive nature, leveraging fuzzy logic control to efficiently balance multiple power sources.Unlike traditional strategies, this approach considers variations in climatic conditions, contributing to improved system resilience.The dual-goal approach, emphasizing immediate power needs and long-term stability, adds a unique dimension compared to existing methods.Simulation results indicate that the proposed IPMC is effective.It successfully maintains power availability and keeps the battery at an optimal charge state.The study involves a comparison between real-time results obtained using an RT-LAB simulator and simulation results from MATLAB/Simulink.The results confirm the effectiveness and     To further advance this research and contribute to the practical implementation of such systems, some future research directions are planned as using adaptive control strategies and conduct a comprehensive techno-economic analysis to evaluate the cost-effectiveness of the proposed system compared to traditional energy sources.

Figure 3 .
Figure 3. Determination of the electrical characteristics.

Figure 9 .
Figure 9. Dynamic model of diesel generator.

Figure 10 .
Figure 10.Proposed intelligent PMC of the studied system.

Figure 12 .
Figure 12.Measurement acquisition device at the laboratory.

Figure 15 .
Figure 15.Profile of the wind speed.

Figure 17 .
Figure 17.Profile of the load power.

Table 1 .
Some common areas of research in this field of HRES.

DC Bus DC DC AC DC AC DC DC AC DC DC Inference engine PV Generator Wind Generator Diesel Generator Batteries Load Power Management Control G Figure 1. Proposed hybrid system with power management control.Table 2 .
To lower peak demand and energy prices, researchers are investigating how consumers can better regulate their electricity usage through strategies such as load shifting, load shedding, and energy-efficient technology Advantages and drawbacks of different types of mathematical models 8 .

Table 4 .
Energies calculations.Significant values are in bold.

Table 5 .
Panels and wind turbine number calculation.

Table 7 .
Input of the fuzzy regulator.

Table 8 .
The various operating modes.